Performance Evaluation of Cloudera Impala GA

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Performance Evaluation of
Cloudera impala 1.0
May 1, 2013
CELLANT Corp. R&D Strategy Division
Yukinori SUDA
@sudabon

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v Support for a subset of ANSI-‐‑‒92 SQL
v CREATE, ALTER, SELECT, INSERT, JOIN, and subqueries
v Support for partitioned joins, fully distributed aggregations, and
fully distributed top-‐‑‒n queries
v Support for a variety of data formats:
v Hadoop native (Apache Avro, SequenceFile, RCFile with Snappy, GZIP, BZIP,
or uncompressed)
v text (uncompressed or LZO-‐‑‒compressed)
v Parquet (Snappy or uncompressed)
v Support for all CDH4 64-‐‑‒bit packages:
v RHEL 6.2/5.7, Ubuntu, Debian, SLES
v Connectivity via JDBC, ODBC, Hue GUI, or command-‐‑‒line shell
v Kerberos authentication and MR/Impala resource isolation
v etc
Cloudera Impala GA was released !!
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Our System Environment
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v Install using Cloudera Manager Free Edition 4.5.2
Master Slave
11 Servers
All servers are connected with 1Gbps Ethernet through an L2 switch
Active
NameNode
DataNode
TaskTracker
Impalad
Stand-‐‑‒by
NameNode
JobTracker
statestored
3 Servers
DataNode
TaskTracker
Impalad
DataNode
TaskTracker
Impalad
DataNode
TaskTracker
Impalad
DataNode
TaskTracker
Impalad
DataNode
TaskTracker
Impalad
DataNode
TaskTracker
Impalad
DataNode
TaskTracker
Impalad
DataNode
TaskTracker
Impalad
DataNode
TaskTracker
Impalad
DataNode
TaskTracker
Impalad

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v CPU
l Intel Core 2 Duo 2.13 GHz with Hyper Threading
v Memory
l 4GB
v Disk
l 7,200 rpm SATA mechanical Hard Disk Drive * 1
v OS
l Cent OS 6.2
Our “wimpy” Server Specification
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v Use CDH4.2.1 + Impala version 1.0
v Use hivebench in open-‐‑‒sourced benchmark tool “HiBench”
l https://github.com/hibench
v Modified datasets to 1/10 scale
l Default configuration generates table with 1 billion rows
v Modified query sentence
l Deleted “INSERT INTO TABLE …” to evaluate read-‐‑‒only performance
v Combines a few storage format with a few compression method
l TextFile, SequenceFile, RCFile, ParquestFile
l No compression, Gzip, Snappy
v Comparison with job query latency
v Average job latency over 5 measurements
Benchmark
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• Uservisits table
– 100 million rows
– 16,895 MB as TextFile
– Table Definitions
• sourceIP string
• destURL string
• visitDate string
• adRevenue double
• userAgent string
• countryCode string
• languageCode string
• searchWord string
• duration int
• Rankings table
– 12 million rows
– 744 MB as TextFile
– Table Definitions
• pageURL string
• pageRank int
• avgDuration int
Modified Datasets
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SELECT
sourceIP,
sum(adRevenue) as totalRevenue,
avg(pageRank)
FROM
rankings_̲t R
JOIN (
SELECT
sourceIP,
destURL,
adRevenue
FROM
uservisits_̲t UV
WHERE
(datediff(UV.visitDate, '1999-‐‑‒01-‐‑‒01')>=0
AND
datediff(UV.visitDate, '2000-‐‑‒01-‐‑‒01')<=0)
) NUV
ON
(R.pageURL = NUV.destURL)
group by sourceIP
order by totalRevenue DESC
limit 1;
Modified Query
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Benchmark Result (Hive)
cited from “Performance evaluation of Cloudera impala 0.6 beta...”
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0 50 100 150 200 250
No Comp.
Gzip
Snappy
Gzip
Snappy
TextFileSequenceFileRCFile
235.843
227.883
213.616
234.289
197.894
Avg. Job Latency [sec]

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Benchmark Result (Impala)
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0 50 100 150 200 250
No Comp.
Gzip
Snappy
Gzip
Snappy
Snappy
Text
File
Sequence
FileRCFile
Parquet
File
36.61
29.736
24.024
26.083
19.586
16.2
Avg. Job Latency [sec]

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v Exchange the order of JOINed Tables like below
SELECT
sourceIP, sum(adRevenue) as totalRevenue, avg(pageRank)
FROM
(SELECT sourceIP, destURL, adRevenue FROM uservisits_̲ps UV WHERE
(datediff(UV.visitDate, '1999-‐‑‒01-‐‑‒01')>=0 AND datediff(UV.visitDate,
'2000-‐‑‒01-‐‑‒01')<=0)) NUV
JOIN
rankings_̲ps R
ON
(R.pageURL = NUV.destURL)
group by sourceIP
order by totalRevenue DESC
limit 1;
v Result
l Parquet compressed as Snappy: 34.374 sec
Additional Experiments
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v Parquet + Snappy is the fastest
v Specifically,
l ParquetFile compressed as Snappy: 16.2 sec
v Need to take care the order of JOINed tables
v Hope for future extension
l Support UDF
l Window Function
l etc
Conclusion
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Letʼ’s try it out on your envrionment!!
Thanks!